library(phyloseq)
library(dplyr)
library(tidyr)
library(tibble)
library(ggplot2)
library(vegan)
library(readr)
library(ampvis)
load(file = "SOB_files.rda")
# Make a data frame with a column for the read counts of each sample
sample_sum_df <- data.frame(sum = sample_sums(SOB_data))
# Histogram of sample read counts
ggplot(sample_sum_df, aes(x = sum)) +
geom_histogram(color = "black", fill = "indianred", binwidth = 2500) +
ggtitle("Distribution of sample sequencing depth") +
xlab("Read counts") +
theme(axis.title.y = element_blank())
#Standardize abundances to the median sequencing depth
total <- median(sample_sums(SOB_data))
standf <- function(x, t=total) round(t * (x/sum(x)))
SOB_data.std <- transform_sample_counts(SOB_data, standf)
#Filter taxa with cutoff 3.0 Coefficient of Variation
SOB_data.stdf <- filter_taxa(SOB_data.std, function(x) sd(x)/mean(x) > 3.0, TRUE)
fungi.p <- subset_taxa(SOB_data.stdf, Phylum!="p__unidentified")
fungi.p <- tax_glom(fungi.p, taxrank="Phylum")
fungi.p2 <- subset_samples(fungi.p, site_code != "null")
code_names <- c(INV = "Invasive",
PL = "Plantation",
UN = "Native")
bar_phyla_code <- plot_bar(fungi.p2, x = "site_name", fill = "Phylum")
bar_phyla_code + geom_bar(stat = "identity", color="black", size=0.2,position = "stack") +
facet_grid(site_code ~ ., labeller = labeller(site_code = code_names)) +
scale_fill_brewer(type = "div", palette = "Paired")
#Transforming to proportions
SOB_data.prop <- transform_sample_counts(SOB_data.stdf, function(otu) otu/sum(otu))
SOB_data.prop1 <- subset_samples(SOB_data.prop, site_code != "null")
#Palette
library(RColorBrewer)
pal <- colorRampPalette(brewer.pal(12, "Paired"))
#Ordination
ord.SOB <- ordinate(SOB_data.prop1, "NMDS", "bray")
## Run 0 stress 0.3272439
## Run 1 stress 0.324991
## ... New best solution
## ... Procrustes: rmse 0.02641423 max resid 0.2188735
## Run 2 stress 0.3293062
## Run 3 stress 0.3243587
## ... New best solution
## ... Procrustes: rmse 0.02582157 max resid 0.2191589
## Run 4 stress 0.3236486
## ... New best solution
## ... Procrustes: rmse 0.02284304 max resid 0.2224103
## Run 5 stress 0.3253105
## Run 6 stress 0.3243008
## Run 7 stress 0.3273157
## Run 8 stress 0.3272866
## Run 9 stress 0.3275019
## Run 10 stress 0.323585
## ... New best solution
## ... Procrustes: rmse 0.01134536 max resid 0.1814109
## Run 11 stress 0.3250794
## Run 12 stress 0.3368188
## Run 13 stress 0.3235016
## ... New best solution
## ... Procrustes: rmse 0.023272 max resid 0.2216099
## Run 14 stress 0.3245983
## Run 15 stress 0.3234724
## ... New best solution
## ... Procrustes: rmse 0.01872211 max resid 0.2254138
## Run 16 stress 0.325958
## Run 17 stress 0.3308166
## Run 18 stress 0.3358738
## Run 19 stress 0.4182711
## Run 20 stress 0.3250799
## *** No convergence -- monoMDS stopping criteria:
## 3: no. of iterations >= maxit
## 17: stress ratio > sratmax
plot_ordination(SOB_data.prop1, ord.SOB, shape = "site_code", color = "site_name") +
geom_point(size = 3) + scale_color_manual(values = pal(42)) + labs(title = "All samples indicated by site name and code")
#Filtering data
SOB_data.prop.PL <- subset_samples(SOB_data.prop, site_code == "PL")
#Ordination
ord.SOB.PL <- ordinate(SOB_data.prop.PL, "NMDS", "bray")
## Run 0 stress 0.2891296
## Run 1 stress 0.2918815
## Run 2 stress 0.2895129
## ... Procrustes: rmse 0.01247778 max resid 0.113833
## Run 3 stress 0.2899605
## Run 4 stress 0.2907021
## Run 5 stress 0.2895129
## ... Procrustes: rmse 0.01229969 max resid 0.1138403
## Run 6 stress 0.292045
## Run 7 stress 0.2890911
## ... New best solution
## ... Procrustes: rmse 0.02137696 max resid 0.1750331
## Run 8 stress 0.2880312
## ... New best solution
## ... Procrustes: rmse 0.01740416 max resid 0.1754765
## Run 9 stress 0.288901
## Run 10 stress 0.291168
## Run 11 stress 0.2891288
## Run 12 stress 0.2900775
## Run 13 stress 0.2889627
## Run 14 stress 0.3177512
## Run 15 stress 0.2891363
## Run 16 stress 0.2901402
## Run 17 stress 0.2894934
## Run 18 stress 0.2881973
## ... Procrustes: rmse 0.005919558 max resid 0.06167274
## Run 19 stress 0.2877326
## ... New best solution
## ... Procrustes: rmse 0.01087331 max resid 0.1147065
## Run 20 stress 0.2880902
## ... Procrustes: rmse 0.01030379 max resid 0.1138105
## *** No convergence -- monoMDS stopping criteria:
## 20: stress ratio > sratmax
plot_ordination(SOB_data.prop.PL, ord.SOB.PL, color = "state") +
geom_point(size = 3) + scale_color_brewer(type = "div", palette = "Set1",
name = "State",
labels = c("Australian Capital Territory",
"New South Wales",
"South Australia",
"Victoria",
"Western Australia")) +
labs(title = "Plantation sites colored by state")
#Filtering data
SOB_data.prop.UN <- subset_samples(SOB_data.prop,
site_code == "UN" |
site_code == "null")
#Ordination
ord.SOB.UN <- ordinate(SOB_data.prop.UN, "NMDS", "bray")
## Run 0 stress 0.295749
## Run 1 stress 0.2960179
## ... Procrustes: rmse 0.05379557 max resid 0.1848509
## Run 2 stress 0.2894091
## ... New best solution
## ... Procrustes: rmse 0.0649258 max resid 0.2105987
## Run 3 stress 0.2929966
## Run 4 stress 0.2900297
## Run 5 stress 0.291876
## Run 6 stress 0.2920973
## Run 7 stress 0.2882637
## ... New best solution
## ... Procrustes: rmse 0.02047467 max resid 0.09910336
## Run 8 stress 0.2900333
## Run 9 stress 0.2957215
## Run 10 stress 0.3004788
## Run 11 stress 0.2898515
## Run 12 stress 0.3051156
## Run 13 stress 0.2903868
## Run 14 stress 0.2897819
## Run 15 stress 0.3011159
## Run 16 stress 0.2899798
## Run 17 stress 0.2896673
## Run 18 stress 0.290406
## Run 19 stress 0.2898804
## Run 20 stress 0.2889866
## *** No convergence -- monoMDS stopping criteria:
## 20: stress ratio > sratmax
plot_ordination(SOB_data.prop.UN, ord.SOB.UN, color = "state", shape = "site_code") +
geom_point(size = 3) + scale_color_brewer(type = "div", palette = "Set1",
name = "State",
labels = c("Australian Capital Territory",
"New South Wales",
"South Australia",
"Victoria",
"Western Australia")) +
scale_shape_discrete(labels = c("Nullabor", "Native"), name = "Site") +
labs(title = "Native sites, including Nullabor, colored by state")
#Filtering data
SOB_data.prop.UN1 <- subset_samples(SOB_data.prop, site_code == "UN")
#Ordination
ord.SOB.UN1 <- ordinate(SOB_data.prop.UN1, "NMDS", "bray")
## Run 0 stress 0.3250856
## Run 1 stress 0.3153795
## ... New best solution
## ... Procrustes: rmse 0.06878835 max resid 0.2661971
## Run 2 stress 0.3267914
## Run 3 stress 0.3265395
## Run 4 stress 0.3293899
## Run 5 stress 0.3340861
## Run 6 stress 0.3255799
## Run 7 stress 0.3145817
## ... New best solution
## ... Procrustes: rmse 0.03474687 max resid 0.3123634
## Run 8 stress 0.3150517
## ... Procrustes: rmse 0.03472002 max resid 0.3151576
## Run 9 stress 0.31541
## Run 10 stress 0.315621
## Run 11 stress 0.3202786
## Run 12 stress 0.315084
## Run 13 stress 0.3249198
## Run 14 stress 0.314252
## ... New best solution
## ... Procrustes: rmse 0.02134826 max resid 0.09567648
## Run 15 stress 0.3222605
## Run 16 stress 0.3149147
## Run 17 stress 0.3154573
## Run 18 stress 0.3214494
## Run 19 stress 0.3289438
## Run 20 stress 0.315335
## *** No convergence -- monoMDS stopping criteria:
## 1: no. of iterations >= maxit
## 19: stress ratio > sratmax
plot_ordination(SOB_data.prop.UN1, ord.SOB.UN1, color = "state") +
geom_point(size = 3) +
scale_color_brewer(type = "div", palette = "Set1", name = "State",
labels = c("Australian Capital Territory",
"New South Wales",
"South Australia",
"Victoria",
"Western Australia")) +
scale_shape_discrete(labels = c("Nullabor", "Native"), name = "Site") +
labs(title = "Native sites colored by state")
#Filtering data only to plantation and native sites
SOB_data.stdf.1 <- subset_samples(SOB_data.stdf,
site_code == "UN" |
site_code == "PL")
#Heatmap
amp_heatmap(data = SOB_data.stdf.1,
group = c("state", "site_code"),
tax.show = 50,
scale.seq = 100,
plot.text.size = 2,
tax.aggregate = "Genus",
tax.add = "Family")
## Warning: Transformation introduced infinite values in discrete y-axis
Tree1
#Tree visual
set.seed(1)
metacoder::heat_tree_matrix(taxa::filter_taxa(obj, taxon_names == "c__Agaricomycetes", subtaxa = TRUE),
dataset = "diff_table",
node_size = n_obs,
node_label = taxon_names,
node_color = log2_median_ratio,
node_color_range = c("#a6611a","#dfc27d","#bdbdbd","#80cdc1","#018571"),
node_color_trans = "linear",
node_label_max = 120,
node_color_interval = c(-1, 1),
edge_color_interval = c(-1, 1),
node_size_axis_label = "Number of OTUs",
node_color_axis_label = "Log2 ratio median proportions",
initial_layout = "reingold-tilford", layout = "davidson-harel")
#Heatmap
ht.type <- amp_heatmap(data = SOB_data.stdf,
group = "site_code",
tax.show = 100,
scale.seq = 100,
plot.text.size = 2,
tax.aggregate = "Genus",
tax.add = "Order")
ht.type + scale_x_discrete(breaks = c("INV", "null", "PL", "UN"),
labels = c("Invasive", "Nullabor", "Plantation", "Native"))
## Warning: Transformation introduced infinite values in discrete y-axis
Plantation
SOB_data.stdf.PL <- subset_samples(SOB_data.stdf, site_code == "PL")
amp_rabund(SOB_data.stdf.PL,
tax.aggregate = "Genus",
tax.add = "Order",
scale.seq = 10,
tax.show = 80,
adjust.zero = 0.1,
plot.log = TRUE)
## 80
Native
SOB_data.stdf.UN <- subset_samples(SOB_data.stdf, site_code == "UN")
amp_rabund(SOB_data.stdf.UN,
tax.aggregate = "Genus",
tax.add = "Order",
scale.seq = 10,
tax.show = 80,
adjust.zero = 0.1,
plot.log = TRUE)
## 80
Invasive
SOB_data.stdf.INV <- subset_samples(SOB_data.stdf, site_code == "INV")
amp_rabund(SOB_data.stdf.INV,
tax.aggregate = "Genus",
tax.add = "Order",
scale.seq = 10,
tax.show = 80,
adjust.zero = 0.1,
plot.log = TRUE)
## 80
TopNOTUs <- function(sample,N) {
names(sort(taxa_sums(sample), TRUE)[1:N])
}
PL.sample <- merge_samples(SOB_data.stdf.PL, "site_code")
INV.sample <- merge_samples(SOB_data.stdf.INV, "site_code")
UN.sample <- merge_samples(SOB_data.stdf.UN, "site_code")
top.PL <- TopNOTUs(PL.sample, 100)
top.INV <- TopNOTUs(INV.sample, 100)
top.UN <- TopNOTUs(UN.sample, 100)
PL.100OTUs <- prune_taxa(top.PL, PL.sample) %>% psmelt()
INV.100OTUs <- prune_taxa(top.INV, INV.sample) %>% psmelt()
UN.100OTUs <- prune_taxa(top.UN, UN.sample) %>% psmelt()
write.csv(PL.100OTUs, file = "top_100OTUs_Plantation.csv")
write.csv(INV.100OTUs, file = "top_100OTUs_Invasive.csv")
write.csv(UN.100OTUs, file = "top_100OTUs_Native.csv")